Combinations Of Adjustable Parameters Research Articles

Total optimization potential (TOP) approach based constrained design of isoprene and cis-abienol production in A. thaliana

Mathematical modelling is an important approach in metabolic engineering projects to support the design and adaptation of organisms for the needs of biotechnology. Assuming sufficient quality of the model, mechanistic mathematical descriptions of the processes enable the prediction of the systemic impact of metabolic pathway engineering on the organism’s metabolism. In this paper, modelling is used to design the necessary changes in Arabidopsis thaliana metabolism for the production of cis-abienol and isoprene. We developed a kinetic model of the A. thaliana 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, including the production of plastoquinone, chlorophyll side chains and carotenoids. For the purpose of optimisation, it was extended with additional reactions leading to the production of cis-abienol (using geranylgeranyl diphosphate as precursor) and isoprene (using dimethylallyl pyrophosphate as precursor).A total optimisation potential (TOP) based search of reasonable enzyme concentration adjustments yielded sets of parameter combinations, from which those were selected that gave the largest increase in the product flux compared to parameter sets with a smaller number of parameters. Three designs (sets of required concentration changes of particular enzymes) are suggested for each product, based on optimisation of the 2048 possible combinations of adjustable parameters (enzyme concentrations). The resulting optimization of the model forecasts a cis-abienol production of 0.021 nmol.s−1.ml−1 and an isoprene production of 0.048 nmol.s−1.ml−1 in plastids of A. thaliana.

Comparison of models for the relative static permittivity with the e-CPA equation of state

This study compares five different models of the relative static permittivity when they are used in the electrolyte Cubic Plus Association (e-CPA) equation of state. To get the best possible performance of the models, the parameters of e-CPA are readjusted for every model. Two different combinations of adjustable parameters are tested. The static permittivity models that are compared include both simple correlations and theoretically derived expressions. A new theoretically based model, that has not been applied to e-CPA before, is also investigated. The novel model describes the impact ions have on the relative static permittivity based on water–ion association. The model is parameterized in two ways: firstly, so that the model describes the reported experimental relative static permittivity data, and secondly to describe the permittivity when kinetic depolarization is not included. All the models are tested for their quantitative agreement with mean ionic activity coefficients (MIAC), osmotic coefficients and density. The model that describes the experimental data the best is the one based on ion association, when it is parameterized to describe the experimental relative static permittivity data. The prediction of the individual ion activity coefficients (IIAC) is also investigated. The only model that is capable of describing the qualitative trend of the IIAC data is the ion association model, but the quantitative agreement with the IIAC data is quite poor. Because of this, an additional parameterization of the ion association model is performed based on an altered parameter optimization strategy. It is shown that with the new parameterization it is possible to describe the IIAC data well, without significant loss of performance for any of the other properties.

Metrological control of welded sheet joints

The results of metrological control of welded sheet joints of studies at low-frequency processing are presented. During fusion welding, a field of residual elastic stresses is created in structures, which are extremely unevenly distributed in them. To equalize the stresses, stabilization is performed using special vibration exciters (vibrators) based on low-frequency mechanical vibrations. This provides dimensional stabilization of welded structures made of low carbon steel. The author investigated the process of vibration hardening of welded sheet joints with several combinations of adjustable parameters. As a result, graphs of accelerations and vibration amplitudes of the exciter and the system were obtained. An assessment of the influence of alternating stresses on the value of residual stresses in the welded joint is given. The theoretical studies of the vibration molding process have been confirmed.

EXPERIMENTAL ANALYSIS OF RESISTANCE SPOT WELDING PARAMETERS FOR LOW CARBON STEEL SHEET

Resistance Spot Welding (RSW) is one of the most efficient fusion welding processes which known as a thermo – electric process used in joining metal by a combination of adjustable parameters (heat, pressure and time), it is high speed, flexible and highly productive process. It is extensively used for many industrial purposes in particular in the automotive industry. The aim of the present work is to study the effect of welding nugget area on the tensile strength of the resistance spot welded joints, the present discussion deals with effect of the parameters of resistance spot welding process (current, time, electrode force) on nugget formation and on failure load of the spot weld joints (the relation between nugget size and strength with input parameters). The test material of the specimens which used in this study is low carbon steel. The mechanical performance (peak load, failure mode) of the welded joints was determined by tensile testing, the testing results were analyzed which were obvious the effectiveness and the necessaries of the suitable or proper welding parameters for the strength of the spot weld joints

Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network.

Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization. We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds. Models were assessed by independent validation (I-Val) using newly measured RI values for 1492 compounds. The best model demonstrated an I-Val standard error of 55 RI units and was built using a Ward's clustering data split and a minimally nonlinear network architecture. Use of validation statistics for stopping and final model selection resulted in better independent validation performance than the use of test set statistics.

A note on reverse scheduling with maximum lateness objective

The inverse and reverse counterparts of the single-machine scheduling problem $$1||L_{\max }$$ are studied in [2], in which the complexity classification is provided for various combinations of adjustable parameters (due dates and processing times) and for five different types of norm: $$\ell _{1},\ell _{2},\ell _{\infty },\ell _{H}^{\Sigma } $$ , and $$\ell _{H}^{\max }$$ . It appears that the $$O(n^{2})$$ -time algorithm for the reverse problem with adjustable due dates contains a flaw. In this note, we present the structural properties of the reverse model, establishing a link with the forward scheduling problem with due dates and deadlines. For the four norms $$\ell _{1},\ell _{\infty },\ell _{H}^{\Sigma }$$ , and $$ \ell _{H}^{\max }$$ , the complexity results are derived based on the properties of the corresponding forward problems, while the case of the norm $$\ell _{2}$$ is treated separately. As a by-product, we resolve an open question on the complexity of problem $$1||\sum \alpha _{j}T_{j}^{2}$$ .

Automatic termination of parallel optimization runs of global stochastic optimization methods by upper limit criterion

Many combinations of adjustable parameters should be tested in optimization experiments of biochemical networks to find the smallest subset of parameters enabling the best improvements of objective function both in case of design task and parameter estimation task. In case of optimization with global stochastic optimization methods one of the problems is the termination of the optimization run looking for a good compromise between spent computational resources and probability that the best found value of objective function will be the global optimum. Longer runs increase the possibility to each the global optimum. Automatic termination criteria in case of consensus or stagnation of parallel optimization runs have been proposed as criteria for automatic termination. Varying the consensus and delay time settings different probability of reaching global optimum and duration of optimization can be reached. It is proposed to modify automatic optimization termination criteria of parallel optimization runs applying upper limit agreement of a number of parallel optimization runs. Automatic application of upper limit agreement would reduce the duration of scanning of the whole space of combination of adjustable parameters. This approach is tested on the yeast glycolysis model with six adjustable parameters using COPASI, CoRunner and ConvAn software for five parallel optimization runs per combination of adjustable parameters.

Determinition of best set of adjustable parameters with full search and limited search methods

In case of optimization of biochemical networks the best combination of adjustable parameters has to be found keeping low the costs of modification of biochemical network and reducing the risk of unpredicted side effects of processes outside the scope of the model. Dynamic models of biochemical networks usually are in form of system of differential equations and therefore can not be optimized analytically. Usually they are optimized using computationally demanding global stochastic optimization methods which may have hardly predictable duration of optimization. Optimization of all the possible combinations is necessary to find the best set of adjustable parameters per number of parameters in combination. Due to the combinatorial explosion the full search often is not feasible approach if computational and time resources are limited. In this study the performance of full search is compared with forward selection and three metabolic control based methods which need significantly less combinations to be examined. It is found that forward selection is a good compromise in case of larger number of adjustable parameters where the number of possible combinations exceeds the available resources needed for full search in the adjustable parameter space. It is proposed to combine full search for small number of adjustable parameters with forward selection at larger number of adjustable parameters per combination.

ConvAn: A convergence analyzing tool for optimization of biochemical networks

Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable.Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces.A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale.By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters.The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties.The software ConvAn is freely available on www.biosystems.lv/convan.